As healthcare processes have become increasingly digitized, large volumes of patient data are now generated on human patients at nearly every medical facility for many types of healthcare interactions. This patient data includes significant amounts of medical imaging data that is captured by medical imaging modalities (e.g., an x-ray machine, computed tomography (CT) scanner, magnetic resonance imaging (MRI) machine, and the like). In many medical settings, image data is first captured by the imaging modality, and then later transferred via a network to another location such as a viewing workstation for further analysis, evaluation, or diagnosis by a healthcare provider.
One practical consideration relating to the use of medical imaging data involves the amount of time required to obtain and transfer the medical images over a network, to generate a view of the medical images at a viewing location. This time is often dependent on the network and processing capabilities and computational resources available to transfer and interpret the imaging data. For imaging applications that visualize separate two-dimensional (2D) images, this time may be quite short and is primarily a function of network latency. For more advanced applications that generate visualizations of three-dimensional (3D) images, however, an entire stack of images must typically be transferred before viewing and interacting with the visualizations.
As a result of these and other technical considerations, many transfers of medical imaging data are bandwidth sensitive, which has led to the increased use of compression for imaging data. However, some compression schemes are not appropriate for use with medical imaging use cases, particularly if the compression technique leads to the loss or obscuring of diagnostic detail in the image data. Further, the use of some compression schemes within medical imaging networks often involves a performance and processing overhead that may not result in sufficient savings in time or network resources.